U.S. patent number 10,102,616 [Application Number 15/114,685] was granted by the patent office on 2018-10-16 for method and system for surface wear determination.
This patent grant is currently assigned to Ent. Services Development Corporation LP. The grantee listed for this patent is ENT. SERVICES DEVELOPMENT CORPORATION LP. Invention is credited to Allen J. Chon, Jonathan David Gibson, Joseph Miller.
United States Patent |
10,102,616 |
Miller , et al. |
October 16, 2018 |
Method and system for surface wear determination
Abstract
Implementations of the present disclosure provide a method and
system for surface wear determination. According to one
implementation, an image of an object surface is captured via an
input device. A surface pattern is detected from the captured image
and object data associated with the object surface is identified
based on the detected pattern. Additionally, a surface wear value
of the object surface is determined based on the object data and
surface pattern.
Inventors: |
Miller; Joseph (Boulder,
CO), Gibson; Jonathan David (Austin, TX), Chon; Allen
J. (Sammamish, WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
ENT. SERVICES DEVELOPMENT CORPORATION LP |
Houston |
TX |
US |
|
|
Assignee: |
Ent. Services Development
Corporation LP (Houston, TX)
|
Family
ID: |
53757450 |
Appl.
No.: |
15/114,685 |
Filed: |
January 28, 2014 |
PCT
Filed: |
January 28, 2014 |
PCT No.: |
PCT/US2014/013415 |
371(c)(1),(2),(4) Date: |
July 27, 2016 |
PCT
Pub. No.: |
WO2015/116036 |
PCT
Pub. Date: |
August 06, 2015 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20160343126 A1 |
Nov 24, 2016 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0251 (20130101); G01B 11/22 (20130101); B60C
11/246 (20130101); G06T 7/0006 (20130101); G01M
17/027 (20130101); B60C 2019/004 (20130101); G06T
2207/30164 (20130101); G06Q 30/0207 (20130101); G01B
11/24 (20130101) |
Current International
Class: |
G06T
7/00 (20170101); G01B 11/22 (20060101); B60C
11/24 (20060101); G01M 17/02 (20060101); G06Q
30/02 (20120101); B60C 19/00 (20060101); G01B
11/24 (20060101) |
Field of
Search: |
;382/108 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
101762241 |
|
Jun 2010 |
|
CN |
|
102141385 |
|
Aug 2011 |
|
CN |
|
2141476 |
|
Jan 2010 |
|
EP |
|
2004224227 |
|
Aug 2004 |
|
JP |
|
2009107484 |
|
May 2009 |
|
JP |
|
2009107484 |
|
May 2009 |
|
JP |
|
2011226971 |
|
Nov 2011 |
|
JP |
|
WO2013045594 |
|
Apr 2013 |
|
WO |
|
Other References
Baratis Evdoxios, "Automatic Logo and Trademark Extraction from
Large Corporate Web Sites," 2005, Dissertation Thesis,
<http://www.intelligence.tuc.gr/lib/downloadfile.php?id=247>.
cited by applicant .
Dean Takahashi, "How Google Goggles works to deliver visual search
results for mobile phones," Aug. 23, 2010,
<http://venturebeat.com/2010/08/23/how-google-goggles-works-to-deliver-
-visual-search-results-for-mobile-phones/>. cited by applicant
.
Jeff Salton, "What's in a name? Google Goggles lets you search the
web with pictures," Dec. 8, 2009,
<http://www.gizmag.com/google-goggles-picture-searching/13551/>.
cited by applicant .
Mark Milian, "How Google is teaching computers to see," Apr. 15,
2011,
<http://www.cnn.com/2011/TECH/innovation/04/14/google.goggles/>.
cited by applicant .
MengXiang et al., "What is the algorithm used by Google's reverse
image search (i.e. search by image)?," Jun. 5, 2012-Aug. 31, 2014,
<https://www.quora.com/What-is-the-algorithm-used-by-Googles-reverse-i-
mage-search-i-e-search-by-image>. cited by applicant .
Milan Broum, "Open your eyes: Google Goggles now available on
iPhone in Google Mobile App," Oct. 5, 2010,
<http://googlemobile.blogspot.com/2010/10/open-your-eyes-google-goggle-
s-now.html>. cited by applicant .
Nathan Chandler, "What is Google Goggles?," Jul. 3, 2012,
<http://electronics.howstuffworks.com/gadgets/other-gadgets/google-gog-
gles.htm/printable>. cited by applicant .
Shah et al., "Handwritten Character Recognition using Radial
Histogram," International Journal of Research in Advent Technology,
vol. 2, No. 4, Apr. 2014,
<http://www.ijrat.org/downloads/april-2014/paper%20id-24201433.pdf>-
. cited by applicant .
Thaker et al., "Structural Feature Extraction to recognize some of
the Offline Isolated Handwritten Gujarati Characters using Decision
Tree Classifier," International Journal of Computer Applications,
vol. 99, No. 15, Aug. 2014,
<http://research.ijcaonline.org/volume99/number15/pxc3898381.pdf>.
cited by applicant .
Belongie et al., "Shape Matching and Object Recognition Using Shape
Contexts," Apr. 2002, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 24, No. 24., pp. 509-522. cited by
applicant .
Takacs et al., "Fast Computation of Rotation-Invariant Image
Features by an Approximate Radial Gradient Transform," Aug. 2013,
IEEE Transactions on Image Processing, vol. 22, No. 8, pp.
2970-2982. cited by applicant .
Zhu et al., "Automatic Document Logo Detection," 2007, ICDAR 2007,
Ninth International Conference on Document Analysis and
Recognition, vol. 2.,
<http://www.umiacs.umd.edu/.about.zhugy/LogoDetection_ICDAR2007.pdf>-
;. cited by applicant .
Huang, D-Y. et al.; "Recognition of Tire Tread Patterns Based on
Gabor Wavelets and Support Vector Machine" 2010; 10 pages. cited by
applicant .
PCT Search Report/Written Opinion--Application No.
PCT/US2014/013415 dated Oct. 29, 2014--15 4 pages. cited by
applicant.
|
Primary Examiner: Saini; Amandeep
Attorney, Agent or Firm: Sheppard Mullin Richter &
Hampton LLP
Claims
What is claimed is:
1. A method for determining surface wear comprising: capturing an
image of an object surface via an input device; detecting a surface
pattern from the captured image; identifying object data associated
with the object surface based on the surface pattern and histogram
data for the captured image; and determining a surface wear value
of the object surface based on the object data and the surface
pattern, wherein a remaining tire life is calculated based on the
surface wear value and a minimum tread life that is specified in
the identified object data.
2. The method of claim 1, wherein the identifying the object data
includes retrieving the object data from a database based on the
histogram data for the captured image, and the determining the
surface wear value includes measuring, via a host server, a current
surface depth value of the object surface based the surface pattern
and calculating, via the host server, the surface wear value based
on the current depth value and the object data.
3. The method of claim 2, further comprising: storing information
relating to a plurality of objects in the database, and generating
histogram data for each of the plurality of objects based on the
stored information; wherein the identifying the object data
includes comparing the histogram data associated with the captured
image with the histogram data associated with the plurality of
objects.
4. The method of claim 3, wherein the plurality of objects are
tires and the object surface corresponds to the treading of a given
tire.
5. The method of claim 4, further comprising: calculating a current
tread depth of the given tire based on the surface pattern; wherein
the surface wear value is based on the current tread depth and an
original tread depth value that is specified in the identified
object data.
6. The method of claim 4, further comprising: providing targeted
marketing information to a user associated with the object surface
based on the determined surface wear value.
7. The method of claim 4, wherein the input device is attached to a
vehicle so as to enable continuous monitoring of the tread wear
value of a tire surface.
8. A system for surface wear determination comprising: an input
device to capture an image of an object surface; a database to
store information associated with a plurality of objects; and a
host server to: analyze the captured image to identify a
corresponding object from among the plurality of objects based on a
detected surface pattern in the captured image and histogram data
for the captured image, and determine a surface wear value based on
the surface pattern and the information that is associated with the
corresponding object stored in the database, wherein a remaining
tire life is calculated based on the surface wear value and a
minimum tread life that is specified in the information that is
associated with the corresponding object stored in the
database.
9. The system of claim 8, wherein the database stores marketing
information associated with a plurality of vendors, and wherein the
host server provides relevant marketing information to a user
associated with the input device based on the determined surface
wear value.
10. The system of claim 9, wherein the plurality of the objects are
tires, the object surface corresponds to the treading of a given
tire, and the surface pattern is a tire tread pattern.
11. The system of claim 10, wherein the host server is to calculate
a current tread depth of the given tire based on the surface
pattern, and wherein the surface wear value is based on the
calculated current tread depth and an original depth value that is
specified in the information that is associated with the
corresponding object.
12. The system of claim 10, wherein the host server is to determine
the relevant marketing information based on the identity of the
corresponding object tire identification and provide the relevant
marketing information to the user when the surface value is less
than a recommended value that is specified in the information that
is associated with the corresponding object.
13. A non-transitory computer readable storage medium having stored
executable instructions, that when executed by a processor, cause
the processor to: receive an image of a surface of a tire from an
input device; analyze the image to detect a tread pattern of the
tire from histogram data for the image; determine a current tread
width of the tire based on the tread pattern and the tire
information for the tire; and calculate a tire wear value based on
the current tread width and original depth information that is
specified in the tire information for the tire, wherein a remaining
tire life is calculated based on the tire wear value and a minimum
tread life that is specified in the tire information for the
tire.
14. The non-transitory computer readable storage medium of claim
13, wherein the instructions are to further cause the processor to:
provide marketing information when the remaining life of the tire
is within a predetermined range of a minimum tread value specified
in the retrieved tire information associated with the identified
tire.
15. The non-transitory computer readable storage medium of claim
13, wherein to detect the tread pattern of the tire, the
instructions are further to cause the processor to determine the
histogram data for the image, and wherein to retrieve the tire
information for the tire, the instructions are further to cause the
processor to select the tire information for the tire from among
tire information associated with multiple types of tires by
comparing the histogram data for the image to histogram data
associated with the multiple types of tires.
16. The non-transitory computer readable storage medium of claim
13, wherein the instructions are to further cause the processor to:
determine content of targeted marketing information to be sent to
the input device based on the tire wear value.
17. The non-transitory computer readable storage medium of claim
13, wherein the instructions are to further cause the processor to:
send targeted marketing information to the input device based on a
determination that the tire wear value satisfies a thresholding
criterion.
Description
BACKGROUND
The continuous rise in population and job growth, particularly
within large metropolitan areas, has led to more and more commuters
traversing roads and busy freeways. Today, millions of cars and
trucks are driven thousands of miles throughout the year within a
myriad of weather conditions. Along with engine reliability,
vehicular tires are critical components towards the safety
performance of the vehicle while in transit. As these tires become
more worn over time and its treading nears the wear bars (
2/32.sup.nd of an inch above the tread), continued driving without
replacing the tires can be hazardous and potentially lead to an
unavoidable accident. Consequently, it is imperative that the
surface wear of tires and similar materials are properly assessed
prior to use.
BRIEF DESCRIPTION OF THE DRAWINGS
The features and advantages of the present disclosure as well as
additional features and advantages thereof will be more clearly
understood hereinafter as a result of a detailed description of
implementations when taken in conjunction with the following
drawings in which:
FIG. 1 is a simplified conceptual diagram of a surface wear
determination system according to an example implementation.
FIG. 2 is a simplified block diagram of the surface wear
determination system according to an example implementation.
FIGS. 3A-3D are sample illustrations of the image processing steps
for surface wear determination according to an example
implementation.
FIG. 4 illustrates a sequence diagram of the processing steps for
surface wear determination according to an example
implementation.
FIG. 5 illustrates a simplified flow chart of the processing steps
for surface wear determination according to an example
implementation.
FIG. 6 illustrates another simplified flow chart of the processing
steps for surface wear determination according to an example
implementation.
DETAILED DESCRIPTION OF THE INVENTION
The following discussion is directed to various examples. Although
one or more of these examples may be discussed in detail, the
implementations disclosed should not be interpreted, or otherwise
used, as limiting the scope of the disclosure, including the
claims. In addition, one skilled in the art will understand that
the following description has broad application, and the discussion
of any implementations is meant only to be an example of one
implementation, and not intended to intimate that the scope of the
disclosure, including the claims, is limited to that
implementation. Furthermore, as used herein, the designators "A",
"B" and "N" particularly with respect to the reference numerals in
the drawings, indicate that a number of the particular feature so
designated can be included with examples of the present disclosure.
The designators can represent the same or different numbers of the
particular features.
The figures herein follow a numbering convention in which the first
digit or digits correspond to the drawing figure number and the
remaining digits identify an element or component in the drawing.
Similar elements or components between different figures may be
identified by the user of similar digits. For example, 143 may
reference element "43" in FIG. 1, and a similar element may be
referenced as 243 in FIG. 2. Elements shown in the various figures
herein can be added, exchanged, and/or eliminated so as to provide
a number of additional examples of the present disclosure. In
addition, the proportion and the relative scale of the elements
provided in the figures are intended to illustrate the examples of
the present disclosure, and should not be taken in a limiting
sense.
Prior solutions for measuring surface wear conditions, such as tire
treading; only measure tread depth through use of a manual process
that includes visual check/reading via the naked eye. For example,
a user may insert a coin, thin ruler or gauge in between tire
treads of a vehicle in an attempt to measure tread depth. Another
prior solution requires that the vehicle tire is driven onto a
pliable material where a tire imprint is left on that material for
manual inspection. However, this solution is highly labor-intensive
and time-consuming, and the only information gleaned from the
lengthy process is the tire tread depth. Still other solutions
utilized super-positioning techniques of before and after images to
determine surface wear. However, this solution requires significant
processing steps including matching imaging angles, and often
results in inaccurate detection and wear measurement.
Implementations of the present disclosure provide a method and
system for determining the surface wear of an object. According to
one example. The system helps automate the process of determining
baselines and surface wear (e.g., tire) through use of an input
device such as a camera. In accordance with one implementation, the
image or video taken by the device is transferred to the host
server (e.g., cloud service provider) where the image is processed
using morphology algorithms for pattern matching, while the
orientation and pattern are used to identify the object and measure
the surface wear. Based on the tire wear pattern, the present
configuration can provide a surface wear value in conjunction with
targeted marketing for other vehicle products and services (e.g.,
loyalty and reward programs and marketing campaigns).
Referring now in more detail to the drawings in which like numerals
identify corresponding parts throughout the views, FIG. 1 is a
simplified conceptual diagram of a surface wear determination
system according to an example implementation. The system 100
includes an end user 102 in communication with an input device 105,
a host server 110, and network 125.
End user 105 may represent an individual or device capable of
interfacing with an input device 105. More particularly, the end
user 102 represents any person or object capable of utilizing the
surface determination platform and may include a vehicle owner,
fleet management staff, or a device affixed onto a target vehicle.
According to one implementation. The end user 102 interacts with
input device 105 such as a smartphone, tablet, notebook personal
computer or similar electronic device having an embedded camera or
other image capture mechanism.
Furthermore, server 110 represents a host service provider
configured to analyze surface wear information associated with a
captured image. More particularly, and as will be described in
further detail with reference to the figures below, the host server
110 may receive a captured image from the input device 105 via the
network 125 and determine a surface wear value of the object while
providing targeted marketing information to the end user 102.
FIG. 2 is a simplified block diagram of the surface wear
determination system according to an example implementation. Here,
the system 200 includes an end user 202 and device 205, host server
210, and a marketing and specifications database 220.
As described above, the end user 202 represents a resource or
entity including, but not limited, a vehicle owner, a fleet
management staff, or a device attached to the vehicle. The end user
202 interacts with input device 205 via the interface application
206 to take pictures or videos of the target surface area of an
object (e.g., tire surface). According to one implementation, the
interface application 206 may be configured to capture a picture or
video and transmit the captured media through network 225 to the
host engine or service provider 210.
Service provider 210 represents a computing architecture having at
least one computer system or host server, which may be operational
with numerous other general purpose or special purpose computing
system environments or configurations and may include, but is not
limited to, personal computer systems, server computer systems,
mainframe computer systems, laptop devices, multiprocessor systems,
microprocessor-based systems, network personal computers, and
distributed cloud computing environments that include any of the
above systems or devices, and the like. Moreover, the host server
provider system 210 may be described in the general context of
computer system-executable instructions stored on a computer
readable storage, such as program modules, being executed by a
computer system. Also, the host server or service provider 210
communicates with the marketing and specifications database 220 and
further includes a processing unit 212, marketing management
component 216, and a surface analyzing module 217.
Processor 212 may be, at least one central processing unit (CPU),
at least one semiconductor-based microprocessor, at least one
graphics processing unit (GPU), other hardware devices suitable for
retrieval and execution of instructions stored in machine-readable
storage medium 214, or combinations thereof. For example, the
processor 212 may include multiple cores on a chip, include
multiple cores across multiple chips, multiple cores across
multiple devices, or combinations thereof. Processor 212 may fetch,
decode, and execute instructions to implement the approaches of the
multi-currency payment system. As an alternative or in addition to
retrieving and executing instructions, processor 212 may include at
least one integrated circuit (IC), other control logic, other
electronic circuits, or combinations thereof that include a number
of electronic components for performing the requisite
functionality.
Machine-readable storage medium 214 may be any electronic,
magnetic, optical, or other physical storage device that contains
or stores executable instructions. Thus, machine-readable storage
medium may be, for example, Random Access Memory (RAM), an
Electrically Erasable Programmable Read-Only Memory (EEPROM), a
storage drive, a Compact Disc Read Only Memory (CD-ROM), and the
like. As such, the machine-readable storage medium can be
non-transitory. As described in detail herein, machine-readable
storage medium 214 may be encoded with a series of executable
instructions for determining personalized shop routing options.
The surface wear determination system of the present
implementations is configured to determine wear patterns for, but
not limited to tires, wheels, pulleys, belts, and similar materials
conducive to wear over time. More particularly, the Surface
Analyzing Module 217 of the host server 210 utilizes morphology
algorithms, which are effective for image pattern detection and
depth measurement, and are integrated into a single processing
construct to determine tire baselines and wear values. According to
one implementation, and as will be described in further detail with
reference to FIGS. 3A-3D, the present system 200 utilizes a single
set of equations and constructs to achieve pattern recognition of
images and portions of images with respect to a tire such as the
tread depth, tread width, and tread pattern. To this end, the
Surface Analyzing Module 217 determines tire baseline and tire wear
by utilizing morphology algorithms that provide an output
orientation (e.g., degrees/scale) and histogram associated with the
captured image. The histogram data associated with the image may
then be compared with generated histogram data associated with
object data and an object (e.g., original tire depth for a specific
tire) within the manufacturer database so as to aid in determining
the extent of surface wear.
Upon identification of the object, the Surface Analyzing Module 217
is further configured to orient the image for measurement
processing and perform depth calculations of the surface (e.g.,
tire tread depth) based on known data (e.g., tire diameter).
Certain manufacturing tire specifications such as tire diameter
serve to provide supplemental tire information, in addition to the
images or videos, for establishing the baseline and wear patterns
for determining the surface wear condition of a tire or other
object surface.
The Marketing Management Component 216 utilizes the surface wear
data to determine relevant advertising and marketing campaigns for
other vehicle products and services associated with the captured
image and object surface. For instance, a loyalty and rewards
programs and other discounted sale offerings may be presented to a
user upon determining that their vehicle's tire has considerable
surface wear and needs to be immediately replaced (e.g., surface
wear value below manufacturer's recommended minimum threshold).
The Marketing and Specifications Database 220 stores relevant tire
information, but not limited to tire manufacturer's specifications,
tire wear information and related tire data such as tire tread
width and tire tread pattern for example. The database 220 may also
store advertising and marketing campaign information as it relates
to enabling service offerings from external venders (e.g., loyalty
and rewards programs). Still further, implementations of the
present disclosure may be used to assess and monitor tire wear for
vehicle fleets such that database 220 includes tire wear
information along with fleet information as it relates to managing
and monitoring tires associated with a fleet of vehicles.
In one example, the system 200 provides real-time and continuous
monitoring of tire wear in the event the end user device captures
pictures and/or video while attached to the vehicle. Moreover, the
surface analyzing module and processing unit are configured to
identify tire baselines and wear in a much more effective and
integrated approach using a single processing construct.
Implementations of the present disclosure can consistently and
accurately determine tire tread pattern, width and depth, despite
the fact that most tires are of the same color in addition to the
wide-range of irregularities that manifest due to extreme wear and
usage.
FIGS. 3A and 3B are sample illustrations of the image processing
steps for surface wear determination according to an example
implementation. As shown here, a captured image 330 includes
surface data relating to a tire tread and pattern (e.g.
Goodyear.RTM. F1 Eagle tire). Generally, treads on tires have
repeating patterns embedded within the tire. The make, model,
and/or year of a tire can be identified in part by assessment of
the pattern style. Still further, a specific area of the tire
surface (e.g., truck tire) may be imaged and matched against a
database of truck-only tire patterns thus reducing the false
positive rate on the basis of not including other tires that are
not designed for trucks having similar tread patterns.
Treads 334 may be detectable by contrast due to the physical
characteristics of the tread pattern. In the present example,
treads 334 extend perpendicular from the base face of the tire
(tread that does not protrude perpendicular will likely bend with
pressure and wear unevenly) based on the camera angle used to
capture the image as seen in FIG. 3A.
According to one implementation, the initial process to acquire the
surface baseline includes generation of a monochromatic baseline
image 330a, and several rounds of erosion so as to create a clear
pattern image 330a' with no color gradient as shown in FIG. 3B. As
shown here, the tread pattern in the image 330a will include all of
the discrete pattern features that repeat over the face of the
tire. By optimizing the level of erosion and dilation, application
of morphological processes yields a more controlled image 330a that
can be used to isolate the tread canals within an image of a tire
face.
Each tread pattern may have a small or large area to process, but
the distinction of the tread canals 331 for even a small image (as
shown in FIG. 3A) are clearly noticeable through the imaging
process. After identifying the tread canals 331, canals having
varying gradients can be used to measure depth while some canals
will be less likely to be used due to a lack of gradients for a
typical image based on the angle used to capture the image. Through
identification of at least two points appearing to have gradients,
the user may select one or more points 327 to generate baseline
histograms, which can be used to identify the tire in other images.
Identification of the gradient target(s) 327 by the user may help
in providing both a more accurate method of the gradient canal
detection and an additional monochromatic image 330b' for
establishing tread pattern analysis baselines.
FIG. 3D shows the morphological histogram generation overlay 340.
Though only one overlay is shown, two or more overlays may be used
for each of the points that have gradients in the corresponding
tread canal. The primary inputs relating to the identified tread
canals in FIG. 3C may include the {X, Y} coordinate pair where the
origin, O, of construct, Z, should overlay plane M of the image
(i.e., origin, O, is a coordinate pair set as will be described in
further detail below) as shown in overlay 340. Furthermore, the
total area that is processed is based on the variables PRmin and
PRmax (i.e., 0<PRmin<PRmax). In one example, these variables
help define a minimum area set for PRmin and a maximum area set for
PRmax. The surface analyzing module selects pixels within the image
for tread depth measurement that lie within the identified
boundaries. Since PRmax and PRmax are not merely boundaries but
actual radii for the measurement of circles with origin O, the
identified area set within the boundaries may be utilized for tread
depth processing.
Analysis of the tread canal depth 335 requires first the
identification of the tread pattern as discussed above. Data about
the tire is accessed, and the system focuses on measuring a
specific angle within the tread canal that is visually seen in the
image in FIG. 3A. When measuring the tread canal, the tire must be
identified (via histogram comparison) so that correct proportion
and tread depth data are used, and the light and camera angle must
be such that it is possible to differentiate the tread canal wall
from the rest of the tread.
After the alignment of the baseline pattern with the image is
performed, trigonometry and other standard geometric equations can
be utilized to extrapolate the actual depth 335 of the tread canal.
In one example, the surface analyzing module detects rotation and
scale of the surface pattern as it resides in the image such that
the image data is rotated and scaled to reflect the dimensions of
the baseline pattern. Accordingly, wear of a tire for example will
be properly computed given that the tire surface within the image
has the correct angle so that contrast between tread and tire is
clearly visible and thus measurable.
An accurate measurement may require a pixel to real-world distance
mapping to be performed. Here, data from a scaled histogram may
provide the relative size data needed to understand the image
scale. If the image is smaller or larger by a significant factor,
then the image may be scaled to reflect the factor thus mapping the
size of the extracted tire image to the approximate size of the
original baseline pattern.
The baseline tread pattern images 330a and 330b of FIGS. 3B and 3C
may be used to create monochromatic baseline images 330a' and 330b
for positioning the image such that data not associated with the
tread canal can be removed from the target image, thus leaving a
mapped location where a pixel measurement can be made. When
creating the baseline for a tread, several data elements may be
retrieved from the specification database which provide ease to
calculate the actual tread depth including, but not limited to;
tread depth for a new tire from a manufacturer and the width of
tire tread.
Computing the actual three-dimensional tread depth from the
captured image may require multiple measurement steps by the
surface analyzing module. After identifying the pattern and
orientation (via morphology algorithm), a function, which is
aligned with the origin of the morphology comparison, is rotated
based on the rotation of the image that is being processed. FIG. 3C
depicts the origin of the function overlay 340 with the origin of
the morphology equation origin (Origin, O, of PRMIN/PRMAX). A start
angle (AS) and distance from start (DS) are contained by the
function and direct, with an original orientation, a vector (VS)
that originates at the origin or near the perceived tread depth 335
(near outer-boundary) and extends a specific distance (DS). The
vector VS may be scaled to a scaling factor and rotated by
application of the morphology function.
Using angle measurement and trigonometric equations, the actual
depth vector 335' may be computed from the image. Initially, and in
accordance with one example, the measurement function is aligned
with the baseline image. For example, if origin (O) is 0,0 and the
angle is 90 degrees and the tread length is 10, then a line
stretches length 10 (in pixels) over the original overlay. In
another example, if a surface wear pattern is matched by scale of
0.5 and rotation of 45 degrees counter-clockwise, then the vector,
VS, may be rotated and sized accordingly. For example, the output
vector may have angle of 45 degrees (90-45) and length of 5
(0.5*10).
In one implementation, the position of the end of vector VS may
mark the beginning of the tread measurement in order to calculate
the origin of measurement. For example, the coordinates for vector
VS may be computed as follows: End X coordinate (VSx) for
VS=Sin(AS)*DS+(Origin X) End Y coordinate (VSy) for
VS=Cos(AS)*DS+(Origin Y)
In the present example, the vector start position may be
represented by origin (X,Y) and the vector end position calculated
as coordinate pair (VSx,VSy). Thereafter, the tread canal bonds of
measurement are calculated using the angle and origin defined as
the end of the previous vector definition. For example, the length
of measurement may be 25 pixels and the angle of measurement as 135
degrees. The points of measurement may be calculated using the
perceived depth vector value 335, with each pixel being sampled in
the source image. A scaling factor of 0.5 may be applied, for
example, if the length of measure is 25 pixels in order to provide
a sample measure of 12.5. Furthermore, due to lighting and camera
angle concerns (e.g., based on the vertical and horizontal angles
from the aperture that captures the image of the tire surface),
each sampling may provide two sample measurements. And based on the
orientation of the image with respect to the user/processor, a
determination can be made to use the more accurate measurement of
the two samples.
The actual size of the tire may also be utilized to determine the
tread depth. The pattern detector which detects scale and rotation
in a subset of the tire. The previously identified pattern, which
is used to match the tire tread, is scaled through a defined scale
value. For instance, if the pattern is 2.5 times smaller than the
actual tire size, then it is defined accordingly such that each
pixel of the image can be mapped to a specific measurement (tire
tread is commonly measured in 32nds of an inch or millimeters). In
order to provide the most accurate measurement, the aspect angle
for the image may also be taken into consideration. Because the
image may be captured at multiple angles with respect to the tire
surface (e.g., 15 to 75 degrees), then to determine the actual
length, the vertical and horizontal angle of the captured image may
be calculated for error correction. For example, if the image was
captured with a 45 degree angle horizontally (assuming the tire is
vertical standing on the tread) and a 0 degree vertical angle (the
camera is at the same elevation as the tire tread) the length must
be rotated to account for the aspect incongruence.
As shown in FIG. 3D, the aspect angle or camera angle associated
with the captured image 330 includes the perceived tread depth
length 335. According to one implementation, the aspect angle is
always formed perpendicularly (via vector AB) to the perceived
tread depth length 335. Given the aspect angle and perceived depth
vector 335, which together also provide angles A and B, angle C and
the actual tread depth length AC (335') can be calculated by the
surface analyzing system. More particularly, utilizing the
techniques above, the surface analyzing module and processing unit
may extrapolate the following dimensions from the captured image to
calculate the actual tread life (i.e., surface wear value) of the
tire surface:
TABLE-US-00001 Tire Width/in - 10.9 Min. Tread Depth/In. 3/32 Image
pix/in - 84.2 Min. Tread Depth/pix 32nd in - 7.89 Tread Depth/In. -
11/32 Tread Wall/pix - 15 Tread Depth/pix 32nd in. - 28.95 Tread
Wall image/in. - 0.178 Tread Wall/32nd In. - 5.699 Tread Life -
33.7%
The remaining tread life may be computed as follows: Total Possible
Life=Original life-Minimum life; Current life=Actual Life-Minimum
Life; Current Life/Total Possible Life=% Remaining Life
Furthermore, although a tire and tire wear are used in the present
example, implementations are not limited thereto as the surface
wear determination system may be used for other flexible and
inflexible materials that undergo prolonged stress during use such
as pulleys, belts, wheels, and the like.
FIG. 4 illustrates a sequence diagram of the processing steps for
surface wear determination according to an example implementation.
In block 450, manufacturer specifications for a plurality of
objects are stored along with the marketing and adverting data
associated with one more vendors. For example, minimum tread depth
specifications may be stored in the database through retrieval from
public sources (e.g., internet) or through direct download/upload
from a relevant vendor. In addition, minimum legal limits may be
set as the threshold value for the surface wear depth. Marketing
and advertising data may include rewards programs, discounts and
similar information used to communicate sales of an object
associated with the captured image and the user. In accordance with
one example, histogram data relating to the manufacture
specifications are then generated in block 451. For example, a
graphical representation (or similar visual interpretation of
numerical data) relating to stored pattern information of an object
within the manufacturer database is generated by the processing
unit. The end user and device 405 capture an image of an object
surface in block 452. Next, the captured image is transmitted to
the host service provider for determination of the surface wear.
The image may be transmitted in block 454 automatically upon the
picture being captured or manually uploaded by a user via an
application running on the user device 405.
In block 456, the surface of the image is analyzed and a pattern is
detected using morphology algorithms as discussed above for
example. As mentioned with respect to FIGS. 3A-3D, a monochromatic
threshold may be applied to the image including dilation, erosion
or other morphological steps to generate, along with one or more
points chosen by the user, baseline histogram data associated with
the captured image. Thereafter, the object (e.g., tire) and object
data (e.g., make, model, year, etc.) are identified based on the
detected pattern in block 458. In one example, proper
identification of the tread pattern may require the system to
instruct the end user to confirm whether the object data is correct
(e.g., make, model, and/or year data of a given tire are accurate).
Furthermore, the database may then be queried to retrieve
measurement data associated with the identified object so as to
determine the minimum threshold value of surface wear for the
target object. In block 460, the surface wear of the object is
calculated and in block 462 the remaining surface life is computed
by the surface analyzing module as discussed above with respect to
FIGS. 3A-3D. For example, the minimum threshold surface value from
the vendor or dealer database may be compared with the measured
surface wear value. In one implementation, if the minimum tread
life is less than the current tread life, then a determination is
made that the tire is exceeded its remaining life value; or if the
manufacturer's minimum tread life+(10% of min tread life) is less
than current tread life, a warning may be communicated that the
user has 10% or less tread life remaining on their tire.
Next, marketing information is determined by the marketing
management component in block 464. For example, if the surface wear
value of the tire is less than ten percent, the system may return
marketing information for the most recent sales so that the user
may purchase replacement tires in the near future. On the other
hand, if the surface wear value or remaining surface life is within
a predetermine range (e.g., wear value is greater than thirty
percent but less than fifty percent), then the marketing management
component may search the database for the best tire sales occurring
at a future time or within a certain period of time (e.g., best
sales within the next six months). Lastly, in block 466, the
remaining surface life and/or the most relevant marketing
information are returned to the user for viewing (e.g., displayed
on smartphone).
FIG. 5 illustrates a simplified flow chart of the processing steps
for surface wear determination according to an example
implementation. In segment 502, an image of an object surface is
captured by a camera or other optical sensor or input device and
received by the processing unit of the host server. A surface
pattern associated with the object is detected from the image in
segment 504. Based on an analysis of the captured image including
the detected pattern and histogram data, the host server and
processing unit identify an object (tire make and model) and
associated object data (e.g., tire diameter, minimum
specifications) from the database in segment 506. In segment 508,
the host server and processing unit compute a surface wear value of
object surface based on the image analysis and the object data. For
example, the actual size of the tire (tire diameter), as retrieved
from the database, is used to scale the captured image for
computing accurate measurements of the tread depth. The actual
tread depth is compared with the manufacturers minimum threshold
value to determine the surface wear value of the target object.
FIG. 6 illustrates another simplified flow chart of the processing
steps for surface wear determination according to an example
implementation. In segment 602, the end user captures an image of
the tire surface via an input device. Next, in segment 604, the
captured image is transmitted and received at the host server over
a network. Through detection and analysis (e.g., via morphology
algorithms) of the image tread pattern, tire information such as
the year, make, and model of the tire may be identified through
comparison of histogram data in segment 606. For example, histogram
data (visual representation of baseline tire pattern information)
generated from baseline images and associated with the captured
image may be compared with histogram data generated from the stored
manufacturer data. More particularly, the closer the match between
the image histogram information and the generated manufacturer
histogram data, the more likely the imaged object corresponds with
the object identified in the manufacturer database. Moreover,
matching histogram data is advantageous over known methods as the
disclosed identification step is faster as it requires less
processing steps than prior solutions.
The tread wear percentage or surface wear value is then calculated
in segment 608 based on the current depth measurement of the
surface and stored object values (e.g., tire diameter) associated
with the identified object. According to one example, the surface
or pattern depth may first be measured after the image pattern is
identified and extracted, and then an equation may be applied to
map the range of possible depth values (e.g., from Min to Max) so
as to calculate the current percentage of life remaining on the
object surface (e.g., tread wear).
If the surface wear value is determined to be less than a minimum
threshold value (e.g., less than 10 percent remaining life based on
manufacturer's recommendation or state law) in segment 610, then
targeted and relevant marketing information corresponding with the
identified tire is retrieved from the database in segment 612. The
targeted marketing information is then provided for display on the
user device in step 614.
Similarly, if the wear value is determined to be greater than the
minimum value, then a probable remaining life may be computed in
segment 616 based on the surface wear value and user/driving
information. For example, the annual mileage, weekly travel routes,
road conditions along the most traveled route, geographic
area/weather conditions, and similar factors that affect tire
condition may be input into the system by an operating user or
computed automatically via onboard vehicle sensors, and then used
in the determination of the probable remaining life (i.e., miles or
time frame before the tire reaches the minimum threshold value).
Next, in segment 618, marketing information based on the identified
tire and remaining probable life are determined by the host server
and then provided for display on the user device in segment
620.
Implementations of the present disclosure provide a system and
method for surface wear determination. Moreover, several advantages
are afforded by the examples disclosed herein. For instance, the
process of determining tire baselines and wear are computed
automatically thereby eliminating the need for manual devices and
processes that require detailed knowledge by the end user.
Moreover, automating the surface wear determination process using
portable devices such as smartphones and tablets allows for more
efficient and reliable management of tires on fleet vehicles. Still
further, the present configuration enables new aspects for
marketing other vehicle products and services (e.g., loyalty and
rewards programs) to customers based on the real-time wear
conditions of objects.
Furthermore, while the disclosure has been described with respect
to particular examples, one skilled in the art will recognize that
numerous modifications are possible. Moreover, not all components,
features, structures, characteristics, etc. described and
illustrated herein need be included in a particular example or
implementation. If the specification states a component, feature,
structure, or characteristic "may", "might", "can" or "could" be
included, for example, that particular component, feature,
structure, or characteristic is not required to be included. If the
specification or claim refers to "a" or "an" element, that does not
mean there is only one of the element. If the specification or
claims refer to "an additional" element, that does not preclude
there being more than one of the additional element.
It is to be noted that although some examples have been described
in reference to particular implementations, other implementations
are possible according to some examples. Additionally, the
arrangement o order of elements or other features illustrated in
the drawings or described herein need not be arranged in the
particular way illustrated and described. Many other arrangements
are possible according to some examples.
The techniques are not restricted to the particular details listed
herein. Indeed, those skilled in the art having the benefit of this
disclosure will appreciate that many other variations from the
foregoing description and drawings may be made within the scope of
the present techniques. Accordingly, it is the following claims
including any amendments thereto that define the scope of the
techniques.
* * * * *
References